Metrics question
Which metrics would you track for Uber Pool during the first 6 months after product launch?
- Uber
- Metrics
- Medium
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What this question tests
Ability to build a launch metric framework distinguishing early adoption signals from long term health.
How to approach it
- State the goal of the first 6 months: prove the shared ride model works for both riders and drivers.
- Track adoption metrics: Pool trip requests as a percent of total requests, and repeat usage rate.
- Track marketplace health: match rate (percent of Pool requests successfully paired), and added detour time per rider.
- Track driver side metrics: earnings per hour on Pool versus solo rides, to ensure drivers are not worse off.
- Track a guardrail: rider satisfaction or rating for Pool trips versus solo trips, to catch a bad experience early.
- Set a decision point at 6 months, based on match rate and driver earnings, to decide on scaling or pausing.
What a strong answer includes
- Separates adoption (are people trying Pool) from health (do matched trips actually work well) as two different concerns.
- Names driver earnings per hour explicitly, since a shared ride product that hurts driver pay will fail on supply.
- Gives an illustrative target, e.g. assumes a 40 percent match rate as viable and under 25 percent as a signal to pause.
- Includes a guardrail metric, rider satisfaction, to catch quality degradation that raw adoption numbers would miss.
Common mistakes
- Tracking only rider side adoption and ignoring driver earnings impact.
- No guardrail metric for ride quality or satisfaction.
- Setting no clear decision threshold for what counts as success after 6 months.
Likely follow-up questions
- What match rate would make you consider pausing the product?
- How do you balance rider price savings against driver earnings?
- What would you change if satisfaction dropped but match rate was high?
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More questions from Uber
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop